CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition
Forecasting in large-scale e-commerce marketplaces is increasingly required to support planning: merchants need to evaluate sales outcomes under future action sequences such as budget schedules, rather than passively predicting what happens next. However, most existing time series forecasting (TSF) approaches remain inherently passive. Even when incorporating operational decisions as auxiliary covariates, they typically optimize for correlation-based extrapolation under historical policies. This design suffers from autoregressive inertia and conflates endogenous market evolution with decision-
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- PossiblePossibly related (embedding) · 50%Going Beyond Statistical Forecasts to Estimate Peak Season Demand Through Machine Learning - Supply & Demand Chain Executive →
- LinkedLinked via arxiv author · 85%Junjie Meng →
“CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition”
- LinkedLinked via arxiv author · 85%Ranxu Zhang →
“CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition”
- LinkedLinked via arxiv author · 85%Zi-an Zhang →
“CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition”
- LinkedLinked via arxiv author · 85%Shujun Liu →
“CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition”
- LinkedLinked via arxiv author · 85%Xiaoning Qian →
“CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition”
- LinkedLinked via arxiv author · 85%Xiaozhou Xu →
“CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition”
- LinkedLinked via arxiv author · 85%Yanyong Zhang →
“CEDAR: Controlled and Event-Driven Demand Forecasting via Residual Decomposition”